Overview

Dataset statistics

Number of variables39
Number of observations5249
Missing cells3948
Missing cells (%)1.9%
Duplicate rows14
Duplicate rows (%)0.3%
Total size in memory1.4 MiB
Average record size in memory281.0 B

Variable types

Categorical21
Numeric15
Text1
DateTime1
Boolean1

Alerts

is_single_plan has constant value "False"Constant
Dataset has 14 (0.3%) duplicate rowsDuplicates
Basic Delivery is highly overall correlated with Basic Price and 3 other fieldsHigh correlation
Basic Price is highly overall correlated with Basic Delivery and 3 other fieldsHigh correlation
Basic Revision is highly overall correlated with Premium Revision and 1 other fieldsHigh correlation
Category is highly overall correlated with FieldHigh correlation
Field is highly overall correlated with CategoryHigh correlation
Premium Delivery is highly overall correlated with Basic Delivery and 3 other fieldsHigh correlation
Premium Price is highly overall correlated with Basic Price and 3 other fieldsHigh correlation
Premium Revision is highly overall correlated with Basic Revision and 1 other fieldsHigh correlation
Standard Delivery is highly overall correlated with Basic Delivery and 4 other fieldsHigh correlation
Standard Price is highly overall correlated with Basic Delivery and 4 other fieldsHigh correlation
Standard Revision is highly overall correlated with Basic Revision and 1 other fieldsHigh correlation
Arabic is highly imbalanced (74.5%)Imbalance
Bengali is highly imbalanced (71.8%)Imbalance
Chinese is highly imbalanced (86.8%)Imbalance
Dutch is highly imbalanced (89.5%)Imbalance
English is highly imbalanced (92.4%)Imbalance
Hebrew is highly imbalanced (90.9%)Imbalance
Hindi is highly imbalanced (55.2%)Imbalance
Indonesian is highly imbalanced (90.0%)Imbalance
Italian is highly imbalanced (72.8%)Imbalance
Portuguese is highly imbalanced (82.2%)Imbalance
Punjabi is highly imbalanced (85.4%)Imbalance
Russian is highly imbalanced (76.8%)Imbalance
Turkish is highly imbalanced (89.9%)Imbalance
Ukrainian is highly imbalanced (85.9%)Imbalance
Rating has 82 (1.6%) missing valuesMissing
Member Since has 1259 (24.0%) missing valuesMissing
Avg Response Time has 1352 (25.8%) missing valuesMissing
Last Delivery has 1254 (23.9%) missing valuesMissing
Avg Response Time is highly skewed (γ1 = 20.36477135)Skewed
Basic Revision has 1114 (21.2%) zerosZeros
Standard Revision has 1031 (19.6%) zerosZeros
Premium Revision has 1281 (24.4%) zerosZeros
Last Delivery has 1194 (22.7%) zerosZeros
Order in Queue has 3198 (60.9%) zerosZeros

Reproduction

Analysis started2024-05-27 17:56:02.338422
Analysis finished2024-05-27 17:56:33.206245
Duration30.87 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

Category
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size82.0 KiB
Music & Audio
676 
Programming & Tech
667 
Business
654 
Lifestyle
531 
Data
530 
Other values (5)
2191 

Length

Max length21
Median length17
Mean length13.382359
Min length4

Characters and Unicode

Total characters70244
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowData
2nd rowData
3rd rowData
4th rowData
5th rowData

Common Values

ValueCountFrequency (%)
Music & Audio 676
12.9%
Programming & Tech 667
12.7%
Business 654
12.5%
Lifestyle 531
10.1%
Data 530
10.1%
Writing & Translation 516
9.8%
Graphics & Design 505
9.6%
Video & Animation 484
9.2%
Digital Marketing 354
6.7%
Photography 332
6.3%

Length

2024-05-27T21:26:33.327728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-27T21:26:33.492165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2848
25.2%
music 676
 
6.0%
audio 676
 
6.0%
programming 667
 
5.9%
tech 667
 
5.9%
business 654
 
5.8%
lifestyle 531
 
4.7%
data 530
 
4.7%
translation 516
 
4.6%
writing 516
 
4.6%
Other values (7) 3018
26.7%

Most occurring characters

ValueCountFrequency (%)
i 8276
 
11.8%
6050
 
8.6%
a 4788
 
6.8%
n 4696
 
6.7%
s 4695
 
6.7%
e 3726
 
5.3%
t 3617
 
5.1%
r 3557
 
5.1%
o 3491
 
5.0%
g 3395
 
4.8%
Other values (21) 23953
34.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 70244
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 8276
 
11.8%
6050
 
8.6%
a 4788
 
6.8%
n 4696
 
6.7%
s 4695
 
6.7%
e 3726
 
5.3%
t 3617
 
5.1%
r 3557
 
5.1%
o 3491
 
5.0%
g 3395
 
4.8%
Other values (21) 23953
34.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 70244
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 8276
 
11.8%
6050
 
8.6%
a 4788
 
6.8%
n 4696
 
6.7%
s 4695
 
6.7%
e 3726
 
5.3%
t 3617
 
5.1%
r 3557
 
5.1%
o 3491
 
5.0%
g 3395
 
4.8%
Other values (21) 23953
34.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 70244
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 8276
 
11.8%
6050
 
8.6%
a 4788
 
6.8%
n 4696
 
6.7%
s 4695
 
6.7%
e 3726
 
5.3%
t 3617
 
5.1%
r 3557
 
5.1%
o 3491
 
5.0%
g 3395
 
4.8%
Other values (21) 23953
34.1%

Field
Categorical

HIGH CORRELATION 

Distinct35
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size82.0 KiB
articles-blogposts
 
183
video-editing
 
181
website-development
 
176
singers-vocalists
 
175
mixing-mastering
 
175
Other values (30)
4359 

Length

Max length29
Median length20
Mean length16.021337
Min length5

Characters and Unicode

Total characters84096
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdata-engineering
2nd rowdata-engineering
3rd rowdata-engineering
4th rowdata-engineering
5th rowdata-engineering

Common Values

ValueCountFrequency (%)
articles-blogposts 183
 
3.5%
video-editing 181
 
3.4%
website-development 176
 
3.4%
singers-vocalists 175
 
3.3%
mixing-mastering 175
 
3.3%
sales 174
 
3.3%
mobile-app-services 170
 
3.2%
social-media-design 170
 
3.2%
social-marketing 169
 
3.2%
fashion-design 168
 
3.2%
Other values (25) 3508
66.8%

Length

2024-05-27T21:26:33.701017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
articles-blogposts 183
 
3.5%
video-editing 181
 
3.4%
website-development 176
 
3.4%
singers-vocalists 175
 
3.3%
mixing-mastering 175
 
3.3%
sales 174
 
3.3%
mobile-app-services 170
 
3.2%
social-media-design 170
 
3.2%
social-marketing 169
 
3.2%
business-plans 168
 
3.2%
Other values (25) 3508
66.8%

Most occurring characters

ValueCountFrequency (%)
e 9843
11.7%
s 7683
 
9.1%
i 7661
 
9.1%
a 6496
 
7.7%
n 6372
 
7.6%
t 5858
 
7.0%
- 5523
 
6.6%
o 5023
 
6.0%
r 4086
 
4.9%
g 3658
 
4.3%
Other values (15) 21893
26.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 84096
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 9843
11.7%
s 7683
 
9.1%
i 7661
 
9.1%
a 6496
 
7.7%
n 6372
 
7.6%
t 5858
 
7.0%
- 5523
 
6.6%
o 5023
 
6.0%
r 4086
 
4.9%
g 3658
 
4.3%
Other values (15) 21893
26.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 84096
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 9843
11.7%
s 7683
 
9.1%
i 7661
 
9.1%
a 6496
 
7.7%
n 6372
 
7.6%
t 5858
 
7.0%
- 5523
 
6.6%
o 5023
 
6.0%
r 4086
 
4.9%
g 3658
 
4.3%
Other values (15) 21893
26.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 84096
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 9843
11.7%
s 7683
 
9.1%
i 7661
 
9.1%
a 6496
 
7.7%
n 6372
 
7.6%
t 5858
 
7.0%
- 5523
 
6.6%
o 5023
 
6.0%
r 4086
 
4.9%
g 3658
 
4.3%
Other values (15) 21893
26.0%

Seller Level
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.0 KiB
1
1441 
2
1352 
3
1325 
4
1131 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5249
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
1 1441
27.5%
2 1352
25.8%
3 1325
25.2%
4 1131
21.5%

Length

2024-05-27T21:26:33.905144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-27T21:26:34.096378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 1441
27.5%
2 1352
25.8%
3 1325
25.2%
4 1131
21.5%

Most occurring characters

ValueCountFrequency (%)
1 1441
27.5%
2 1352
25.8%
3 1325
25.2%
4 1131
21.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5249
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1441
27.5%
2 1352
25.8%
3 1325
25.2%
4 1131
21.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5249
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1441
27.5%
2 1352
25.8%
3 1325
25.2%
4 1131
21.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5249
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1441
27.5%
2 1352
25.8%
3 1325
25.2%
4 1131
21.5%

Seller In Same Level
Real number (ℝ)

Distinct114
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6104.4267
Minimum1
Maximum120000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.5 KiB
2024-05-27T21:26:34.365247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile38
Q1329
median1000
Q34600
95-th percentile33000
Maximum120000
Range119999
Interquartile range (IQR)4271

Descriptive statistics

Standard deviation15455.355
Coefficient of variation (CV)2.5318273
Kurtosis28.381426
Mean6104.4267
Median Absolute Deviation (MAD)900
Skewness4.9206409
Sum32042136
Variance2.3886798 × 108
MonotonicityNot monotonic
2024-05-27T21:26:34.725313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 205
 
3.9%
2800 172
 
3.3%
2000 129
 
2.5%
313 120
 
2.3%
1800 87
 
1.7%
2700 86
 
1.6%
606 86
 
1.6%
12000 84
 
1.6%
2200 83
 
1.6%
8900 82
 
1.6%
Other values (104) 4115
78.4%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 2
 
< 0.1%
7 6
 
0.1%
9 9
 
0.2%
18 19
 
0.4%
22 17
 
0.3%
23 55
1.0%
25 23
0.4%
27 25
0.5%
32 25
0.5%
ValueCountFrequency (%)
120000 47
0.9%
75000 43
0.8%
53000 42
0.8%
50472 46
0.9%
44000 42
0.8%
33000 46
0.9%
26000 45
0.9%
23000 45
0.9%
19000 45
0.9%
17000 44
0.8%

Basic Price
Real number (ℝ)

HIGH CORRELATION 

Distinct242
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.37673
Minimum3.24
Maximum10529.63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size82.0 KiB
2024-05-27T21:26:35.025239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.24
5-th percentile5
Q115
median40
Q3100
95-th percentile395
Maximum10529.63
Range10526.39
Interquartile range (IQR)85

Descriptive statistics

Standard deviation404.47399
Coefficient of variation (CV)3.3051544
Kurtosis254.5953
Mean122.37673
Median Absolute Deviation (MAD)30
Skewness13.303904
Sum642355.45
Variance163599.21
MonotonicityNot monotonic
2024-05-27T21:26:35.392276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 621
 
11.8%
5 416
 
7.9%
100 377
 
7.2%
30 345
 
6.6%
15 304
 
5.8%
50 275
 
5.2%
20 273
 
5.2%
25 217
 
4.1%
150 180
 
3.4%
40 148
 
2.8%
Other values (232) 2093
39.9%
ValueCountFrequency (%)
3.24 5
 
0.1%
4.48 36
 
0.7%
5 416
7.9%
6.49 1
 
< 0.1%
8.95 50
 
1.0%
8.96 1
 
< 0.1%
9.73 1
 
< 0.1%
10 621
11.8%
13.44 30
 
0.6%
15 304
5.8%
ValueCountFrequency (%)
10529.63 1
 
< 0.1%
10000 1
 
< 0.1%
9248.15 1
 
< 0.1%
7500 1
 
< 0.1%
6500 1
 
< 0.1%
4000 4
0.1%
3800 1
 
< 0.1%
3750 1
 
< 0.1%
3600 1
 
< 0.1%
3556.59 2
< 0.1%

Standard Price
Real number (ℝ)

HIGH CORRELATION 

Distinct375
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean283.56588
Minimum6.49
Maximum18496.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size82.0 KiB
2024-05-27T21:26:35.643695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.49
5-th percentile15
Q140
median95
Q3225
95-th percentile1000
Maximum18496.3
Range18489.81
Interquartile range (IQR)185

Descriptive statistics

Standard deviation801.43854
Coefficient of variation (CV)2.8262869
Kurtosis137.28638
Mean283.56588
Median Absolute Deviation (MAD)65
Skewness9.5540037
Sum1488437.3
Variance642303.74
MonotonicityNot monotonic
2024-05-27T21:26:35.890861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 305
 
5.8%
20 225
 
4.3%
150 215
 
4.1%
30 204
 
3.9%
25 200
 
3.8%
100 198
 
3.8%
40 183
 
3.5%
60 167
 
3.2%
200 163
 
3.1%
10 162
 
3.1%
Other values (365) 3227
61.5%
ValueCountFrequency (%)
6.49 1
 
< 0.1%
8.95 14
 
0.3%
8.96 1
 
< 0.1%
9.73 3
 
0.1%
10 162
3.1%
12.98 1
 
< 0.1%
13.44 17
 
0.3%
15 161
3.1%
16.22 1
 
< 0.1%
17.91 18
 
0.3%
ValueCountFrequency (%)
18496.3 1
< 0.1%
15581.48 1
< 0.1%
15000 1
< 0.1%
12000 1
< 0.1%
10670.37 1
< 0.1%
10000 1
< 0.1%
9750 1
< 0.1%
8000 2
< 0.1%
7500 1
< 0.1%
7400 1
< 0.1%

Premium Price
Real number (ℝ)

HIGH CORRELATION 

Distinct470
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean544.64435
Minimum12.98
Maximum35566.67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size82.0 KiB
2024-05-27T21:26:36.135432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12.98
5-th percentile25
Q175
median160
Q3400
95-th percentile2006
Maximum35566.67
Range35553.69
Interquartile range (IQR)325

Descriptive statistics

Standard deviation1590.2678
Coefficient of variation (CV)2.919828
Kurtosis142.42722
Mean544.64435
Median Absolute Deviation (MAD)110
Skewness9.5627565
Sum2858838.2
Variance2528951.7
MonotonicityNot monotonic
2024-05-27T21:26:36.390246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 320
 
6.1%
150 205
 
3.9%
200 201
 
3.8%
50 186
 
3.5%
300 146
 
2.8%
250 142
 
2.7%
500 138
 
2.6%
30 136
 
2.6%
60 129
 
2.5%
120 117
 
2.2%
Other values (460) 3529
67.2%
ValueCountFrequency (%)
12.98 3
 
0.1%
13.44 7
 
0.1%
15 79
1.5%
16.22 1
 
< 0.1%
17.91 9
 
0.2%
20 100
1.9%
22.39 10
 
0.2%
22.4 1
 
< 0.1%
25 93
1.8%
26.86 12
 
0.2%
ValueCountFrequency (%)
35566.67 2
< 0.1%
29307.41 1
 
< 0.1%
20000 4
0.1%
15000 3
0.1%
14229.63 2
< 0.1%
13659.26 1
 
< 0.1%
13500 1
 
< 0.1%
12000 1
 
< 0.1%
10800 1
 
< 0.1%
10670.37 1
 
< 0.1%

Basic Delivery
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0779196
Minimum1
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.5 KiB
2024-05-27T21:26:36.615171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile14
Maximum90
Range89
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.7981182
Coefficient of variation (CV)1.1766093
Kurtosis39.405466
Mean4.0779196
Median Absolute Deviation (MAD)1
Skewness4.7086338
Sum21405
Variance23.021938
MonotonicityNot monotonic
2024-05-27T21:26:36.779404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
2 1305
24.9%
3 1109
21.1%
1 1105
21.1%
5 442
 
8.4%
7 440
 
8.4%
4 322
 
6.1%
10 182
 
3.5%
14 152
 
2.9%
6 66
 
1.3%
30 64
 
1.2%
Other values (4) 62
 
1.2%
ValueCountFrequency (%)
1 1105
21.1%
2 1305
24.9%
3 1109
21.1%
4 322
 
6.1%
5 442
 
8.4%
6 66
 
1.3%
7 440
 
8.4%
10 182
 
3.5%
14 152
 
2.9%
21 56
 
1.1%
ValueCountFrequency (%)
90 1
 
< 0.1%
60 2
 
< 0.1%
45 3
 
0.1%
30 64
 
1.2%
21 56
 
1.1%
14 152
 
2.9%
10 182
3.5%
7 440
8.4%
6 66
 
1.3%
5 442
8.4%

Standard Delivery
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3476853
Minimum1
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.5 KiB
2024-05-27T21:26:36.962402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q37
95-th percentile21
Maximum90
Range89
Interquartile range (IQR)4

Descriptive statistics

Standard deviation7.1002254
Coefficient of variation (CV)1.1185535
Kurtosis21.407823
Mean6.3476853
Median Absolute Deviation (MAD)2
Skewness3.8209271
Sum33319
Variance50.4132
MonotonicityNot monotonic
2024-05-27T21:26:37.188109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
3 992
18.9%
2 864
16.5%
5 721
13.7%
7 628
12.0%
4 574
10.9%
10 352
 
6.7%
1 323
 
6.2%
14 297
 
5.7%
6 177
 
3.4%
21 145
 
2.8%
Other values (6) 176
 
3.4%
ValueCountFrequency (%)
1 323
 
6.2%
2 864
16.5%
3 992
18.9%
4 574
10.9%
5 721
13.7%
6 177
 
3.4%
7 628
12.0%
10 352
 
6.7%
14 297
 
5.7%
15 2
 
< 0.1%
ValueCountFrequency (%)
90 1
 
< 0.1%
75 2
 
< 0.1%
60 16
 
0.3%
45 22
 
0.4%
30 133
 
2.5%
21 145
 
2.8%
15 2
 
< 0.1%
14 297
5.7%
10 352
6.7%
7 628
12.0%

Premium Delivery
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.6174509
Minimum1
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.5 KiB
2024-05-27T21:26:37.412139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median6
Q310
95-th percentile30
Maximum90
Range89
Interquartile range (IQR)7

Descriptive statistics

Standard deviation11.605001
Coefficient of variation (CV)1.2066607
Kurtosis17.308914
Mean9.6174509
Median Absolute Deviation (MAD)3
Skewness3.6102942
Sum50482
Variance134.67604
MonotonicityNot monotonic
2024-05-27T21:26:37.574771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
7 885
16.9%
3 715
13.6%
5 686
13.1%
10 560
10.7%
4 459
8.7%
14 430
8.2%
2 390
7.4%
30 324
 
6.2%
6 224
 
4.3%
1 223
 
4.2%
Other values (8) 353
 
6.7%
ValueCountFrequency (%)
1 223
 
4.2%
2 390
7.4%
3 715
13.6%
4 459
8.7%
5 686
13.1%
6 224
 
4.3%
7 885
16.9%
8 2
 
< 0.1%
10 560
10.7%
14 430
8.2%
ValueCountFrequency (%)
90 32
 
0.6%
75 9
 
0.2%
60 42
 
0.8%
45 61
 
1.2%
30 324
6.2%
28 1
 
< 0.1%
21 205
 
3.9%
20 1
 
< 0.1%
14 430
8.2%
10 560
10.7%

Basic Revision
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.44141741
Minimum-1
Maximum9
Zeros1114
Zeros (%)21.2%
Negative1925
Negative (%)36.7%
Memory size61.5 KiB
2024-05-27T21:26:37.773018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median0
Q31
95-th percentile3
Maximum9
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5480266
Coefficient of variation (CV)3.5069451
Kurtosis3.1346189
Mean0.44141741
Median Absolute Deviation (MAD)1
Skewness1.393018
Sum2317
Variance2.3963864
MonotonicityNot monotonic
2024-05-27T21:26:37.932260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
-1 1925
36.7%
0 1114
21.2%
1 1060
20.2%
2 657
 
12.5%
3 322
 
6.1%
5 99
 
1.9%
4 41
 
0.8%
9 16
 
0.3%
6 8
 
0.2%
7 5
 
0.1%
ValueCountFrequency (%)
-1 1925
36.7%
0 1114
21.2%
1 1060
20.2%
2 657
 
12.5%
3 322
 
6.1%
4 41
 
0.8%
5 99
 
1.9%
6 8
 
0.2%
7 5
 
0.1%
8 2
 
< 0.1%
ValueCountFrequency (%)
9 16
 
0.3%
8 2
 
< 0.1%
7 5
 
0.1%
6 8
 
0.2%
5 99
 
1.9%
4 41
 
0.8%
3 322
 
6.1%
2 657
12.5%
1 1060
20.2%
0 1114
21.2%

Standard Revision
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.75328634
Minimum-1
Maximum9
Zeros1031
Zeros (%)19.6%
Negative1925
Negative (%)36.7%
Memory size61.5 KiB
2024-05-27T21:26:38.116586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median0
Q32
95-th percentile5
Maximum9
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.9503901
Coefficient of variation (CV)2.589175
Kurtosis1.9309514
Mean0.75328634
Median Absolute Deviation (MAD)1
Skewness1.3129312
Sum3954
Variance3.8040217
MonotonicityNot monotonic
2024-05-27T21:26:38.322533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
-1 1925
36.7%
0 1031
19.6%
2 834
15.9%
1 581
 
11.1%
3 454
 
8.6%
5 206
 
3.9%
4 114
 
2.2%
9 36
 
0.7%
6 31
 
0.6%
7 24
 
0.5%
ValueCountFrequency (%)
-1 1925
36.7%
0 1031
19.6%
1 581
 
11.1%
2 834
15.9%
3 454
 
8.6%
4 114
 
2.2%
5 206
 
3.9%
6 31
 
0.6%
7 24
 
0.5%
8 13
 
0.2%
ValueCountFrequency (%)
9 36
 
0.7%
8 13
 
0.2%
7 24
 
0.5%
6 31
 
0.6%
5 206
 
3.9%
4 114
 
2.2%
3 454
8.6%
2 834
15.9%
1 581
11.1%
0 1031
19.6%

Premium Revision
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.81806058
Minimum-1
Maximum9
Zeros1281
Zeros (%)24.4%
Negative1925
Negative (%)36.7%
Memory size61.5 KiB
2024-05-27T21:26:38.536397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median0
Q32
95-th percentile5
Maximum9
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.157978
Coefficient of variation (CV)2.6379195
Kurtosis1.9779352
Mean0.81806058
Median Absolute Deviation (MAD)1
Skewness1.4412189
Sum4294
Variance4.6568689
MonotonicityNot monotonic
2024-05-27T21:26:38.721856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
-1 1925
36.7%
0 1281
24.4%
2 578
 
11.0%
3 553
 
10.5%
1 369
 
7.0%
5 242
 
4.6%
4 133
 
2.5%
9 65
 
1.2%
7 42
 
0.8%
6 37
 
0.7%
ValueCountFrequency (%)
-1 1925
36.7%
0 1281
24.4%
1 369
 
7.0%
2 578
 
11.0%
3 553
 
10.5%
4 133
 
2.5%
5 242
 
4.6%
6 37
 
0.7%
7 42
 
0.8%
8 24
 
0.5%
ValueCountFrequency (%)
9 65
 
1.2%
8 24
 
0.5%
7 42
 
0.8%
6 37
 
0.7%
5 242
 
4.6%
4 133
 
2.5%
3 553
10.5%
2 578
11.0%
1 369
 
7.0%
0 1281
24.4%

Rating
Real number (ℝ)

MISSING 

Distinct19
Distinct (%)0.4%
Missing82
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean4.9366751
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size82.0 KiB
2024-05-27T21:26:38.930553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.8
Q14.9
median5
Q35
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.15819349
Coefficient of variation (CV)0.032044541
Kurtosis254.01381
Mean4.9366751
Median Absolute Deviation (MAD)0
Skewness-12.463904
Sum25507.8
Variance0.02502518
MonotonicityNot monotonic
2024-05-27T21:26:39.139684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
5 3157
60.1%
4.9 1450
27.6%
4.8 353
 
6.7%
4.7 110
 
2.1%
4.6 29
 
0.6%
4.5 19
 
0.4%
4.3 15
 
0.3%
4 8
 
0.2%
4.4 6
 
0.1%
4.2 4
 
0.1%
Other values (9) 16
 
0.3%
(Missing) 82
 
1.6%
ValueCountFrequency (%)
1 3
 
0.1%
2.8 1
 
< 0.1%
3 3
 
0.1%
3.2 2
 
< 0.1%
3.3 1
 
< 0.1%
3.5 1
 
< 0.1%
3.7 1
 
< 0.1%
3.8 1
 
< 0.1%
4 8
0.2%
4.1 3
 
0.1%
ValueCountFrequency (%)
5 3157
60.1%
4.9 1450
27.6%
4.8 353
 
6.7%
4.7 110
 
2.1%
4.6 29
 
0.6%
4.5 19
 
0.4%
4.4 6
 
0.1%
4.3 15
 
0.3%
4.2 4
 
0.1%
4.1 3
 
0.1%

Rating Count
Real number (ℝ)

Distinct873
Distinct (%)16.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean209.45275
Minimum1
Maximum12860
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size82.0 KiB
2024-05-27T21:26:39.373534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q114
median43
Q3164
95-th percentile882
Maximum12860
Range12859
Interquartile range (IQR)150

Descriptive statistics

Standard deviation608.22343
Coefficient of variation (CV)2.9038694
Kurtosis147.91509
Mean209.45275
Median Absolute Deviation (MAD)38
Skewness9.84063
Sum1099417.5
Variance369935.74
MonotonicityNot monotonic
2024-05-27T21:26:39.657168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 164
 
3.1%
2 155
 
3.0%
3 143
 
2.7%
5 114
 
2.2%
4 99
 
1.9%
7 94
 
1.8%
11 85
 
1.6%
12 83
 
1.6%
15 82
 
1.6%
13 80
 
1.5%
Other values (863) 4150
79.1%
ValueCountFrequency (%)
1 164
3.1%
2 155
3.0%
3 143
2.7%
4 99
1.9%
5 114
2.2%
6 78
1.5%
7 94
1.8%
8 71
1.4%
9 68
1.3%
9.5 2
 
< 0.1%
ValueCountFrequency (%)
12860 1
< 0.1%
12396 1
< 0.1%
11201 1
< 0.1%
10938 1
< 0.1%
10732 1
< 0.1%
9729 1
< 0.1%
5971 1
< 0.1%
5961 1
< 0.1%
5800 1
< 0.1%
5396 1
< 0.1%
Distinct112
Distinct (%)2.1%
Missing1
Missing (%)< 0.1%
Memory size82.0 KiB
2024-05-27T21:26:40.009084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length19
Mean length8.7370427
Min length4

Characters and Unicode

Total characters45852
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)0.4%

Sample

1st rowPakistan
2nd rowPakistan
3rd rowPakistan
4th rowUnited States
5th rowUnited Kingdom
ValueCountFrequency (%)
pakistan 1241
19.1%
united 1008
15.5%
states 665
 
10.3%
bangladesh 461
 
7.1%
india 380
 
5.9%
kingdom 318
 
4.9%
nigeria 131
 
2.0%
germany 113
 
1.7%
sri 108
 
1.7%
lanka 108
 
1.7%
Other values (117) 1952
30.1%
2024-05-27T21:26:40.576367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 6953
15.2%
n 4808
 
10.5%
i 4445
 
9.7%
t 4020
 
8.8%
e 3516
 
7.7%
s 2704
 
5.9%
d 2565
 
5.6%
k 1507
 
3.3%
P 1363
 
3.0%
1237
 
2.7%
Other values (42) 12734
27.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45852
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 6953
15.2%
n 4808
 
10.5%
i 4445
 
9.7%
t 4020
 
8.8%
e 3516
 
7.7%
s 2704
 
5.9%
d 2565
 
5.6%
k 1507
 
3.3%
P 1363
 
3.0%
1237
 
2.7%
Other values (42) 12734
27.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45852
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 6953
15.2%
n 4808
 
10.5%
i 4445
 
9.7%
t 4020
 
8.8%
e 3516
 
7.7%
s 2704
 
5.9%
d 2565
 
5.6%
k 1507
 
3.3%
P 1363
 
3.0%
1237
 
2.7%
Other values (42) 12734
27.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45852
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 6953
15.2%
n 4808
 
10.5%
i 4445
 
9.7%
t 4020
 
8.8%
e 3516
 
7.7%
s 2704
 
5.9%
d 2565
 
5.6%
k 1507
 
3.3%
P 1363
 
3.0%
1237
 
2.7%
Other values (42) 12734
27.8%

Member Since
Date

MISSING 

Distinct149
Distinct (%)3.7%
Missing1259
Missing (%)24.0%
Memory size82.0 KiB
Minimum2011-03-01 00:00:00
Maximum2024-05-01 00:00:00
2024-05-27T21:26:40.863017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:41.071849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Avg Response Time
Real number (ℝ)

MISSING  SKEWED 

Distinct34
Distinct (%)0.9%
Missing1352
Missing (%)25.8%
Infinite0
Infinite (%)0.0%
Mean4.3679754
Minimum1
Maximum696
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size82.0 KiB
2024-05-27T21:26:41.303933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile13
Maximum696
Range695
Interquartile range (IQR)2

Descriptive statistics

Standard deviation18.241537
Coefficient of variation (CV)4.1761996
Kurtosis614.20078
Mean4.3679754
Median Absolute Deviation (MAD)0
Skewness20.364771
Sum17022
Variance332.75368
MonotonicityNot monotonic
2024-05-27T21:26:41.519718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
1 2348
44.7%
2 470
 
9.0%
3 271
 
5.2%
4 199
 
3.8%
5 118
 
2.2%
6 85
 
1.6%
7 51
 
1.0%
8 49
 
0.9%
9 42
 
0.8%
24 42
 
0.8%
Other values (24) 222
 
4.2%
(Missing) 1352
25.8%
ValueCountFrequency (%)
1 2348
44.7%
2 470
 
9.0%
3 271
 
5.2%
4 199
 
3.8%
5 118
 
2.2%
6 85
 
1.6%
7 51
 
1.0%
8 49
 
0.9%
9 42
 
0.8%
10 29
 
0.6%
ValueCountFrequency (%)
696 1
 
< 0.1%
312 1
 
< 0.1%
288 3
 
0.1%
264 1
 
< 0.1%
168 1
 
< 0.1%
144 6
 
0.1%
120 1
 
< 0.1%
96 7
 
0.1%
72 14
 
0.3%
48 36
0.7%

Last Delivery
Real number (ℝ)

MISSING  ZEROS 

Distinct26
Distinct (%)0.7%
Missing1254
Missing (%)23.9%
Infinite0
Infinite (%)0.0%
Mean20.461577
Minimum0
Maximum1095
Zeros1194
Zeros (%)22.7%
Negative0
Negative (%)0.0%
Memory size82.0 KiB
2024-05-27T21:26:41.791517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q37
95-th percentile120
Maximum1095
Range1095
Interquartile range (IQR)7

Descriptive statistics

Standard deviation62.641761
Coefficient of variation (CV)3.0614337
Kurtosis49.1998
Mean20.461577
Median Absolute Deviation (MAD)2
Skewness5.7447295
Sum81744
Variance3923.9902
MonotonicityNot monotonic
2024-05-27T21:26:42.001173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 1194
22.7%
1 564
10.7%
7 411
 
7.8%
2 341
 
6.5%
3 241
 
4.6%
14 195
 
3.7%
30 185
 
3.5%
4 170
 
3.2%
5 129
 
2.5%
21 119
 
2.3%
Other values (16) 446
 
8.5%
(Missing) 1254
23.9%
ValueCountFrequency (%)
0 1194
22.7%
1 564
10.7%
2 341
 
6.5%
3 241
 
4.6%
4 170
 
3.2%
5 129
 
2.5%
6 62
 
1.2%
7 411
 
7.8%
14 195
 
3.7%
21 119
 
2.3%
ValueCountFrequency (%)
1095 1
 
< 0.1%
730 4
 
0.1%
365 31
0.6%
360 1
 
< 0.1%
330 12
 
0.2%
300 22
0.4%
270 9
 
0.2%
240 22
0.4%
210 40
0.8%
180 20
0.4%

Order in Queue
Real number (ℝ)

ZEROS 

Distinct51
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6572681
Minimum0
Maximum215
Zeros3198
Zeros (%)60.9%
Negative0
Negative (%)0.0%
Memory size61.5 KiB
2024-05-27T21:26:42.255774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile7
Maximum215
Range215
Interquartile range (IQR)1

Descriptive statistics

Standard deviation6.1279303
Coefficient of variation (CV)3.6976096
Kurtosis483.04287
Mean1.6572681
Median Absolute Deviation (MAD)0
Skewness17.767115
Sum8699
Variance37.551529
MonotonicityNot monotonic
2024-05-27T21:26:42.475278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3198
60.9%
1 760
 
14.5%
2 421
 
8.0%
3 237
 
4.5%
4 154
 
2.9%
5 106
 
2.0%
6 82
 
1.6%
7 50
 
1.0%
8 43
 
0.8%
11 31
 
0.6%
Other values (41) 167
 
3.2%
ValueCountFrequency (%)
0 3198
60.9%
1 760
 
14.5%
2 421
 
8.0%
3 237
 
4.5%
4 154
 
2.9%
5 106
 
2.0%
6 82
 
1.6%
7 50
 
1.0%
8 43
 
0.8%
9 20
 
0.4%
ValueCountFrequency (%)
215 1
< 0.1%
164 1
< 0.1%
161 1
< 0.1%
82 1
< 0.1%
76 1
< 0.1%
72 1
< 0.1%
63 1
< 0.1%
56 2
< 0.1%
54 1
< 0.1%
52 1
< 0.1%

is_single_plan
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size46.1 KiB
False
5249 
ValueCountFrequency (%)
False 5249
100.0%
2024-05-27T21:26:42.666124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Arabic
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size82.0 KiB
0
5024 
1
 
225

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5249
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5024
95.7%
1 225
 
4.3%

Length

2024-05-27T21:26:42.828180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-27T21:26:42.965159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 5024
95.7%
1 225
 
4.3%

Most occurring characters

ValueCountFrequency (%)
0 5024
95.7%
1 225
 
4.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5249
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5024
95.7%
1 225
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5249
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5024
95.7%
1 225
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5249
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5024
95.7%
1 225
 
4.3%

Bengali
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size82.0 KiB
0
4992 
1
 
257

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5249
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4992
95.1%
1 257
 
4.9%

Length

2024-05-27T21:26:43.108678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-27T21:26:43.301626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4992
95.1%
1 257
 
4.9%

Most occurring characters

ValueCountFrequency (%)
0 4992
95.1%
1 257
 
4.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5249
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4992
95.1%
1 257
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5249
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4992
95.1%
1 257
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5249
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4992
95.1%
1 257
 
4.9%

Chinese
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size82.0 KiB
0
5153 
1
 
96

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5249
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5153
98.2%
1 96
 
1.8%

Length

2024-05-27T21:26:43.545936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-27T21:26:43.720286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 5153
98.2%
1 96
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 5153
98.2%
1 96
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5249
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5153
98.2%
1 96
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5249
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5153
98.2%
1 96
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5249
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5153
98.2%
1 96
 
1.8%

Dutch
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size82.0 KiB
0
5177 
1
 
72

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5249
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5177
98.6%
1 72
 
1.4%

Length

2024-05-27T21:26:43.892713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-27T21:26:44.035507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 5177
98.6%
1 72
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 5177
98.6%
1 72
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5249
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5177
98.6%
1 72
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5249
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5177
98.6%
1 72
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5249
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5177
98.6%
1 72
 
1.4%

English
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size82.0 KiB
1
5200 
0
 
49

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5249
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 5200
99.1%
0 49
 
0.9%

Length

2024-05-27T21:26:44.271947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-27T21:26:44.445468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 5200
99.1%
0 49
 
0.9%

Most occurring characters

ValueCountFrequency (%)
1 5200
99.1%
0 49
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5249
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 5200
99.1%
0 49
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5249
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 5200
99.1%
0 49
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5249
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 5200
99.1%
0 49
 
0.9%

French
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size82.0 KiB
0
4480 
1
769 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5249
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4480
85.3%
1 769
 
14.7%

Length

2024-05-27T21:26:44.613733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-27T21:26:44.797799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4480
85.3%
1 769
 
14.7%

Most occurring characters

ValueCountFrequency (%)
0 4480
85.3%
1 769
 
14.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5249
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4480
85.3%
1 769
 
14.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5249
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4480
85.3%
1 769
 
14.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5249
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4480
85.3%
1 769
 
14.7%

German
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size82.0 KiB
0
4567 
1
682 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5249
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4567
87.0%
1 682
 
13.0%

Length

2024-05-27T21:26:45.006206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-27T21:26:45.165164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4567
87.0%
1 682
 
13.0%

Most occurring characters

ValueCountFrequency (%)
0 4567
87.0%
1 682
 
13.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5249
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4567
87.0%
1 682
 
13.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5249
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4567
87.0%
1 682
 
13.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5249
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4567
87.0%
1 682
 
13.0%

Hebrew
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size82.0 KiB
0
5188 
1
 
61

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5249
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5188
98.8%
1 61
 
1.2%

Length

2024-05-27T21:26:45.334141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-27T21:26:45.503546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 5188
98.8%
1 61
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 5188
98.8%
1 61
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5249
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5188
98.8%
1 61
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5249
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5188
98.8%
1 61
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5249
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5188
98.8%
1 61
 
1.2%

Hindi
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size82.0 KiB
0
4759 
1
490 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5249
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4759
90.7%
1 490
 
9.3%

Length

2024-05-27T21:26:45.646855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-27T21:26:45.798227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4759
90.7%
1 490
 
9.3%

Most occurring characters

ValueCountFrequency (%)
0 4759
90.7%
1 490
 
9.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5249
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4759
90.7%
1 490
 
9.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5249
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4759
90.7%
1 490
 
9.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5249
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4759
90.7%
1 490
 
9.3%

Indonesian
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size82.0 KiB
0
5181 
1
 
68

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5249
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5181
98.7%
1 68
 
1.3%

Length

2024-05-27T21:26:45.987084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-27T21:26:46.144750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 5181
98.7%
1 68
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 5181
98.7%
1 68
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5249
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5181
98.7%
1 68
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5249
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5181
98.7%
1 68
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5249
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5181
98.7%
1 68
 
1.3%

Italian
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size82.0 KiB
0
5004 
1
 
245

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5249
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5004
95.3%
1 245
 
4.7%

Length

2024-05-27T21:26:46.336933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-27T21:26:46.481733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 5004
95.3%
1 245
 
4.7%

Most occurring characters

ValueCountFrequency (%)
0 5004
95.3%
1 245
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5249
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5004
95.3%
1 245
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5249
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5004
95.3%
1 245
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5249
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5004
95.3%
1 245
 
4.7%

Portuguese
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size82.0 KiB
0
5108 
1
 
141

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5249
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5108
97.3%
1 141
 
2.7%

Length

2024-05-27T21:26:46.680895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-27T21:26:46.825613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 5108
97.3%
1 141
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0 5108
97.3%
1 141
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5249
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5108
97.3%
1 141
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5249
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5108
97.3%
1 141
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5249
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5108
97.3%
1 141
 
2.7%

Punjabi
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size82.0 KiB
0
5140 
1
 
109

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5249
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5140
97.9%
1 109
 
2.1%

Length

2024-05-27T21:26:47.033977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-27T21:26:47.208551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 5140
97.9%
1 109
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 5140
97.9%
1 109
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5249
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5140
97.9%
1 109
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5249
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5140
97.9%
1 109
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5249
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5140
97.9%
1 109
 
2.1%

Russian
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size82.0 KiB
0
5051 
1
 
198

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5249
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5051
96.2%
1 198
 
3.8%

Length

2024-05-27T21:26:47.384911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-27T21:26:47.570824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 5051
96.2%
1 198
 
3.8%

Most occurring characters

ValueCountFrequency (%)
0 5051
96.2%
1 198
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5249
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5051
96.2%
1 198
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5249
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5051
96.2%
1 198
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5249
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5051
96.2%
1 198
 
3.8%

Spanish
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size82.0 KiB
0
4201 
1
1048 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5249
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4201
80.0%
1 1048
 
20.0%

Length

2024-05-27T21:26:47.720788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-27T21:26:47.927163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4201
80.0%
1 1048
 
20.0%

Most occurring characters

ValueCountFrequency (%)
0 4201
80.0%
1 1048
 
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5249
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4201
80.0%
1 1048
 
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5249
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4201
80.0%
1 1048
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5249
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4201
80.0%
1 1048
 
20.0%

Turkish
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size82.0 KiB
0
5180 
1
 
69

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5249
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5180
98.7%
1 69
 
1.3%

Length

2024-05-27T21:26:48.090210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-27T21:26:48.247110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 5180
98.7%
1 69
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 5180
98.7%
1 69
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5249
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5180
98.7%
1 69
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5249
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5180
98.7%
1 69
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5249
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5180
98.7%
1 69
 
1.3%

Ukrainian
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size82.0 KiB
0
5144 
1
 
105

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5249
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5144
98.0%
1 105
 
2.0%

Length

2024-05-27T21:26:48.413512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-27T21:26:48.616652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 5144
98.0%
1 105
 
2.0%

Most occurring characters

ValueCountFrequency (%)
0 5144
98.0%
1 105
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5249
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5144
98.0%
1 105
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5249
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5144
98.0%
1 105
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5249
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5144
98.0%
1 105
 
2.0%

Urdu
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size82.0 KiB
0
4532 
1
717 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5249
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4532
86.3%
1 717
 
13.7%

Length

2024-05-27T21:26:48.794775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-27T21:26:48.998575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4532
86.3%
1 717
 
13.7%

Most occurring characters

ValueCountFrequency (%)
0 4532
86.3%
1 717
 
13.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5249
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4532
86.3%
1 717
 
13.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5249
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4532
86.3%
1 717
 
13.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5249
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4532
86.3%
1 717
 
13.7%

Interactions

2024-05-27T21:26:29.505280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:05.245694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:06.626379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:08.199543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:10.125447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:11.905336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:13.628169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:15.213218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:16.818983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:18.189978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:19.523124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:20.976900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:23.126391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:25.436526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:27.507740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:29.667361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:05.344392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:06.710066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:08.307267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:10.226258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:12.010117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:13.748338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:15.303202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:16.933646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:18.279152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:19.613327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:21.093091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:23.267931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:25.605109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:27.630943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:29.845623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:05.432421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:06.795891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:08.403845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:10.330164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:12.121536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:13.841141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:15.399858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:17.042987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:18.359762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:19.741488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:21.503768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:23.402734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:25.770745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:27.745349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:29.999872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:05.518288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:06.882341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:08.505196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:10.463137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:12.238574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:13.948371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:15.516885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:17.144459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:18.460404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:19.853747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:21.622113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:23.570741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:25.955793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:27.869601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:30.161483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:05.622367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:06.966830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:08.607558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:10.605229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:12.346425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:14.044765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:15.630956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:17.245062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:18.558111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:19.949507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:21.743427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:23.731421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:26.142901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:27.988595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:30.294947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:05.720298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:07.040786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:08.696990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:10.721131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:12.445362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:14.133901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:15.730975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:17.334408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:18.641961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:20.032600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:21.865814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:23.864360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:26.282835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:28.097917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:30.431788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:05.817983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:07.125950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:08.784620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:10.839218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:12.548306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:14.219697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:15.837970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:17.418810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:18.729532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:20.111962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:21.978731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:23.996509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:26.412126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:28.208064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:30.564825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:05.912793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:07.204079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:08.880697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:10.942518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:12.656895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:14.297473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:15.935182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:17.501736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:18.819732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:20.197343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:22.090698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:24.124023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:26.537087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:28.324022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:30.699681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:05.999278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:07.296982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:09.244636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:11.036013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:12.777110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:14.381862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:16.077701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:17.583784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:18.907107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:20.279659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:22.202423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:24.282929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:26.656048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:28.429422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:30.833068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:06.088694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:07.530338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:09.373183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:11.136491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:12.893811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:14.463031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:16.194418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:17.661931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:18.991109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:20.361178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:22.314389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:24.419500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:26.780666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:28.553521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:30.967997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:06.180330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:07.631386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:09.507355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:11.261250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:13.011771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:14.550115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:16.278416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:17.742760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:19.071482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:20.447404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:22.435372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:24.552764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:26.895624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:28.746223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:31.128122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:06.268381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:07.807065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:09.648782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:11.388027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:13.139353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:14.646512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:16.389714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:17.839515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:19.170911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:20.547301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:22.575871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:24.713836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:27.019322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:28.920450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:31.263258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:06.352592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:07.910816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:09.776119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:11.507569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:13.272595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:14.731335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:16.504413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:17.928767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:19.258749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:20.654336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:22.717947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:24.952799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:27.138167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:29.082558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:31.413735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:06.447717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:08.013964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:09.910283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:11.673017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:13.393713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:15.040909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:16.618472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:18.023656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:19.355686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:20.769271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:22.857886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:25.125641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:27.271708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:29.235951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:31.529601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:06.536903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:08.105445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:10.030324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:11.792743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:13.509183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:15.127707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:16.722323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:18.108073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:19.438857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:20.881161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:22.989082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:25.288648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:27.391514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-27T21:26:29.373318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-27T21:26:49.863678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ArabicAvg Response TimeBasic DeliveryBasic PriceBasic RevisionBengaliCategoryChineseDutchEnglishFieldFrenchGermanHebrewHindiIndonesianItalianLast DeliveryOrder in QueuePortuguesePremium DeliveryPremium PricePremium RevisionPunjabiRatingRating CountRussianSeller In Same LevelSeller LevelSpanishStandard DeliveryStandard PriceStandard RevisionTurkishUkrainianUrdu
Arabic1.000-0.057-0.060-0.038-0.0170.0070.0890.0000.0000.0000.1190.1740.0050.0000.0240.0150.0330.002-0.0350.029-0.025-0.028-0.0310.021-0.033-0.0300.0050.0620.0820.000-0.034-0.031-0.0110.0000.0230.064
Avg Response Time-0.0571.0000.2030.1620.0270.0000.0260.0000.0000.0000.0540.0000.0000.0000.0000.0000.0000.130-0.0610.1340.1260.0970.0410.0000.1080.0080.000-0.0700.0860.0300.1630.1180.0190.0000.0000.000
Basic Delivery-0.0600.2031.0000.5430.0810.0200.0930.0000.0000.0000.1570.0300.0400.0000.0600.0000.0350.0520.0780.0000.6760.4530.0850.0000.1310.0180.000-0.1590.0970.0200.8350.5010.0800.0000.0000.040
Basic Price-0.0380.1620.5431.0000.1380.0000.0450.0000.0490.0000.0630.0170.0000.0000.0000.0000.0000.156-0.0880.0000.4610.8440.1580.0000.204-0.1410.063-0.2810.0380.0170.5250.9100.1480.0420.0300.000
Basic Revision-0.0170.0270.0810.1381.0000.0670.1670.0300.0000.0180.1950.0170.0410.0000.0280.0510.0140.0060.0410.0420.0510.0770.8810.0330.0240.1080.0000.0860.0000.0000.0690.0990.9460.0000.0240.044
Bengali0.0070.0000.0200.0000.0671.0000.1760.0160.0070.0110.2700.0560.0000.0860.2170.0170.037-0.0410.0370.0320.002-0.038-0.0490.027-0.0870.0210.0350.0880.0400.034-0.028-0.054-0.0390.0180.0260.056
Category0.0890.0260.0930.0450.1670.1761.0000.0960.0480.0630.9980.1170.0820.0460.1910.1150.0740.0660.0970.0760.0210.0030.2880.1080.0740.0980.1140.1720.0620.0840.0450.0130.2870.0420.0950.303
Chinese0.0000.0000.0000.0000.0300.0160.0961.0000.0000.0000.1200.0120.0000.0000.0390.0250.0230.022-0.0350.012-0.031-0.0110.0070.0060.018-0.0180.000-0.0000.0160.039-0.019-0.0120.0080.0000.0000.000
Dutch0.0000.0000.0000.0490.0000.0070.0480.0001.0000.0000.0980.0730.0970.0000.0260.0000.009-0.0010.0070.000-0.0020.021-0.0070.0000.0190.0000.0000.0020.0140.0260.0040.023-0.0090.0000.0000.027
English0.0000.0000.0000.0000.0180.0110.0630.0000.0001.0000.0520.0150.0180.0000.0240.0000.000-0.0690.0220.0000.0150.0170.0370.000-0.0090.0550.0000.0030.0430.0000.0140.0140.0450.0000.0000.027
Field0.1190.0540.1570.0630.1950.2700.9980.1200.0980.0521.0000.1620.1540.0820.2050.1350.105-0.0680.1460.117-0.003-0.0050.0380.114-0.0180.1220.1930.1870.1110.178-0.013-0.0160.0560.0810.1310.325
French0.1740.0000.0300.0170.0170.0560.1170.0120.0730.0150.1621.0000.3290.0000.0710.0330.082-0.0320.0110.008-0.024-0.021-0.0200.053-0.060-0.0230.0230.1020.0710.276-0.046-0.041-0.0080.0100.0560.057
German0.0050.0000.0400.0000.0410.0000.0820.0000.0970.0180.1540.3291.0000.0000.0790.0000.051-0.0620.0530.000-0.018-0.013-0.0270.053-0.060-0.0080.0000.0820.0560.237-0.026-0.022-0.0190.0240.0210.077
Hebrew0.0000.0000.0000.0000.0000.0860.0460.0000.0000.0000.0820.0000.0001.0000.0330.0000.0000.0010.0030.0000.0200.018-0.0070.000-0.029-0.0180.0000.0000.0100.0050.0040.012-0.0120.0000.0000.038
Hindi0.0240.0000.0600.0000.0280.2170.1910.0390.0260.0240.2050.0710.0790.0331.0000.0310.059-0.011-0.0060.0450.0280.022-0.0030.162-0.045-0.0110.0600.0540.0000.0860.0070.0050.0070.0250.0410.166
Indonesian0.0150.0000.0000.0000.0510.0170.1150.0250.0000.0000.1350.0330.0000.0000.0311.0000.000-0.0290.0410.001-0.010-0.0640.0450.000-0.0030.0470.012-0.0060.0210.049-0.004-0.0650.0550.0000.0000.041
Italian0.0330.0000.0350.0000.0140.0370.0740.0230.0090.0000.1050.0820.0510.0000.0590.0001.0000.0330.0140.024-0.000-0.0070.0160.0260.029-0.0010.0000.0200.0320.129-0.004-0.0080.0070.0000.0070.072
Last Delivery0.0020.1300.0520.1560.006-0.0410.0660.022-0.001-0.069-0.068-0.032-0.0620.001-0.011-0.0290.0331.000-0.4930.0060.0670.1570.0320.0230.092-0.4450.0460.0760.1970.0000.0650.1540.0190.0230.0210.000
Order in Queue-0.035-0.0610.078-0.0880.0410.0370.097-0.0350.0070.0220.1460.0110.0530.003-0.0060.0410.014-0.4931.0000.0000.047-0.1030.0140.043-0.0980.4510.0000.0410.0600.0070.069-0.0940.0200.0000.0000.000
Portuguese0.0290.1340.0000.0000.0420.0320.0760.0120.0000.0000.1170.0080.0000.0000.0450.0010.0240.0060.0001.000-0.020-0.0160.0320.0150.0150.0200.026-0.0050.0170.151-0.011-0.0190.0270.0000.0140.052
Premium Delivery-0.0250.1260.6760.4610.0510.0020.021-0.031-0.0020.015-0.003-0.024-0.0180.0200.028-0.010-0.0000.0670.047-0.0201.0000.5800.0780.0360.103-0.0540.000-0.0590.0720.0310.9120.5550.0770.0000.0000.055
Premium Price-0.0280.0970.4530.8440.077-0.0380.003-0.0110.0210.017-0.005-0.021-0.0130.0180.022-0.064-0.0070.157-0.103-0.0160.5801.0000.1070.0000.169-0.1510.018-0.2150.0490.0320.5670.9610.0990.0000.0000.000
Premium Revision-0.0310.0410.0850.1580.881-0.0490.2880.007-0.0070.0370.038-0.020-0.027-0.007-0.0030.0450.0160.0320.0140.0320.0780.1071.0000.0490.0490.0790.0270.0480.0190.0000.0900.1290.9200.0000.0380.092
Punjabi0.0210.0000.0000.0000.0330.0270.1080.0060.0000.0000.1140.0530.0530.0000.1620.0000.0260.0230.0430.0150.0360.0000.0491.000-0.0230.0040.0210.0080.0000.063-0.023-0.011-0.0010.0000.0080.313
Rating-0.0330.1080.1310.2040.024-0.0870.0740.0180.019-0.009-0.018-0.060-0.060-0.029-0.045-0.0030.0290.092-0.0980.0150.1030.1690.049-0.0231.000-0.0590.000-0.1080.0570.0000.1240.1820.0220.0000.0000.020
Rating Count-0.0300.0080.018-0.1410.1080.0210.098-0.0180.0000.0550.122-0.023-0.008-0.018-0.0110.047-0.001-0.4450.4510.020-0.054-0.1510.0790.004-0.0591.0000.000-0.0350.1000.029-0.025-0.1440.0900.0000.0000.000
Russian0.0050.0000.0000.0630.0000.0350.1140.0000.0000.0000.1930.0230.0000.0000.0600.0120.0000.0460.0000.0260.0000.0180.0270.0210.0000.0001.000-0.0400.0060.0300.0160.016-0.0080.0410.3320.064
Seller In Same Level0.062-0.070-0.159-0.2810.0860.0880.172-0.0000.0020.0030.1870.1020.0820.0000.054-0.0060.0200.0760.041-0.005-0.059-0.2150.0480.008-0.108-0.035-0.0401.0000.2670.013-0.103-0.2400.0750.0340.0000.084
Seller Level0.0820.0860.0970.0380.0000.0400.0620.0160.0140.0430.1110.0710.0560.0100.0000.0210.0320.1970.0600.0170.0720.0490.0190.0000.0570.1000.0060.2671.0000.0630.1390.1890.0020.0170.0420.063
Spanish0.0000.0300.0200.0170.0000.0340.0840.0390.0260.0000.1780.2760.2370.0050.0860.0490.1290.0000.0070.1510.0310.0320.0000.0630.0000.0290.0300.0130.0631.000-0.021-0.0340.0170.0220.0370.104
Standard Delivery-0.0340.1630.8350.5250.069-0.0280.045-0.0190.0040.014-0.013-0.046-0.0260.0040.007-0.004-0.0040.0650.069-0.0110.9120.5670.090-0.0230.124-0.0250.016-0.1030.139-0.0211.0000.5860.0880.0000.0000.015
Standard Price-0.0310.1180.5010.9100.099-0.0540.013-0.0120.0230.014-0.016-0.041-0.0220.0120.005-0.065-0.0080.154-0.094-0.0190.5550.9610.129-0.0110.182-0.1440.016-0.2400.189-0.0340.5861.0000.1180.0000.0000.000
Standard Revision-0.0110.0190.0800.1480.946-0.0390.2870.008-0.0090.0450.056-0.008-0.019-0.0120.0070.0550.0070.0190.0200.0270.0770.0990.920-0.0010.0220.090-0.0080.0750.0020.0170.0880.1181.0000.0000.0200.072
Turkish0.0000.0000.0000.0420.0000.0180.0420.0000.0000.0000.0810.0100.0240.0000.0250.0000.0000.0230.0000.0000.0000.0000.0000.0000.0000.0000.0410.0340.0170.0220.0000.0000.0001.0000.0000.031
Ukrainian0.0230.0000.0000.0300.0240.0260.0950.0000.0000.0000.1310.0560.0210.0000.0410.0000.0070.0210.0000.0140.0000.0000.0380.0080.0000.0000.3320.0000.0420.0370.0000.0000.0200.0001.0000.053
Urdu0.0640.0000.0400.0000.0440.0560.3030.0000.0270.0270.3250.0570.0770.0380.1660.0410.0720.0000.0000.0520.0550.0000.0920.3130.0200.0000.0640.0840.0630.1040.0150.0000.0720.0310.0531.000

Missing values

2024-05-27T21:26:32.176438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-27T21:26:32.786098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-05-27T21:26:33.094246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CategoryFieldSeller LevelSeller In Same LevelBasic PriceStandard PricePremium PriceBasic DeliveryStandard DeliveryPremium DeliveryBasic RevisionStandard RevisionPremium RevisionRatingRating CountCountryMember SinceAvg Response TimeLast DeliveryOrder in Queueis_single_planArabicBengaliChineseDutchEnglishFrenchGermanHebrewHindiIndonesianItalianPortuguesePunjabiRussianSpanishTurkishUkrainianUrdu
0Datadata-engineering2293447.76626.86891.04714142225.03.0PakistanNaNNaNNaN0False000010000000000001
1Datadata-engineering2293250.00450.00950.003571255.05.0PakistanNaNNaNNaN0False000010000000000001
2Datadata-engineering229350.00100.00150.001350125.012.0PakistanNaNNaNNaN0False000010000000000001
3Datadata-engineering2293120.00200.00400.0023101225.013.0United StatesNaNNaNNaN0False000010000000000000
4Datadata-engineering2293100.00300.00450.0071071124.68.0United KingdomNaNNaNNaN0False000010000000000000
5Datadata-engineering229330.0050.0095.001231124.922.0PakistanNaNNaNNaN0False000011000000000001
6Datadata-engineering229380.00160.00300.0037143335.019.0BangladeshNaNNaNNaN0False010010000000000000
7Datadata-engineering2293100.002000.0010000.001232375.010.0PakistanNaNNaNNaN0False000010000000000000
8Datadata-engineering2293200.00300.00400.00714211235.02.0United StatesNaNNaNNaN0False000010000000000000
12Datadata-engineering22932133.963556.594981.4835102335.01.0United KingdomNaNNaNNaN0False000010000000001000
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Duplicate rows

Most frequently occurring

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